Electrical liquid crystal integration with VCSEL arrays for tunable orthogonal polarization laser.

Vertical-cavity surface-emitting laser (VCSEL) arrays that can emit orthogonally polarized light have shown broad application potential and market demand in the fields of optical communication, optical sensors, and biomedicine. Two kinds of polarization-switching VCSEL arrays based on anti-parallel (AP) oriented liquid crystal (LC) external cavities and twisted nematic (TN) oriented LC external cavities are proposed in this paper. The light is independently guided by the LC electrically, achieving uniform lasing of linearly polarized light with mutually orthogonal polarization modes and equal power.

3D bi-directional transformer U-Net for medical image segmentation.

As one of the popular deep learning methods, deep convolutional neural networks (DCNNs) have been widely adopted in segmentation tasks and have received positive feedback. However, in segmentation tasks, DCNN-based frameworks are known for their incompetence in dealing with global relations within imaging features. Although several techniques have been proposed to enhance the global reasoning of DCNN, these models are either not able to gain satisfying performances compared with traditional fully-convolutional structures or not capable of utilizing the basic advantages of CNN-based networks (namely the ability of local reasoning).

HIERARCHICAL BRAIN EMBEDDING USING EXPLAINABLE GRAPH LEARNING.

Brain networks have been extensively studied in neuroscience, to better understand human behavior, and to identify and characterize distributed brain abnormalities in neurological and psychiatric conditions. Several deep graph learning models have been proposed for brain network analysis, yet most current models lack interpretability, which makes it hard to gain any heuristic biological insights into the results. In this paper, we propose a new explainable graph learning model, named hierarchical brain embedding (HBE), to extract brain network representations based on the network community structure, yielding interpretable hierarchical patterns.

Signed graph representation learning for functional-to-structural brain network mapping.

MRI-derived brain networks have been widely used to understand functional and structural interactions among brain regions, and factors that affect them, such as brain development and diseases. Graph mining on brain networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. Since brain functional and structural networks describe the brain topology from different perspectives, exploring a representation that combines these cross-modality brain networks has significant clinical implications.

Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model.

Recently, brain networks have been widely adopted to study brain dynamics, brain development, and brain diseases. Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. However, current graph learning techniques have several issues on brain network mining.

Case report: Anti-NMDA receptor encephalitis manifesting as rapid weight loss and abnormal movement disorders with alternating unilateral ptosis and contralateral limb tremor.

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis, associated with immunoglobulin G (IgG) autoantibodies against the GluN1 subunit of the NMDAR, is one of the most common types of autoimmune encephalitis. In patients with anti-NMDAR encephalitis, movement disorders (MDs) are often frequent, mainly presenting as facial dyskinesias and stereotyped movements. The alternating clinical manifestation of limb tremor with unilateral ptosis is rare.

Coexistence of anti-SOX1 and anti-GABAB receptor antibodies with paraneoplastic limbic encephalitis presenting with seizures and memory impairment in small cell lung cancer: A case report.

Purpose: Paraneoplastic neurological syndromes associated with autoantibodies are rare diseases that cause abnormal manifestations of the nervous system. Early diagnosis of paraneoplastic neurological syndromes paves the way for prompt and efficient therapy.

Case Report: we reported a 56-year-old man presenting with seizures and rapidly progressive cognitive impairment diagnosed as paraneoplastic limbic encephalitis (PLE) with anti-SRY-like high-mobility group box-1 (SOX-1) and anti-γ-aminobutyric acid B (GABAB) receptor antibodies and finally confirmed by biopsy as small cell lung cancer (SCLC).

A Hierarchical Graph Learning Model for Brain Network Regression Analysis.

Brain networks have attracted increasing attention due to the potential to better characterize brain dynamics and abnormalities in neurological and psychiatric conditions. Recent years have witnessed enormous successes in deep learning. Many AI algorithms, especially graph learning methods, have been proposed to analyze brain networks.

White Matter Alterations in Depressive Disorder.

Depressive disorder is the most prevalent affective disorder today. Depressive disorder has been linked to changes in the white matter. White matter changes in depressive disorder could be a result of impaired cerebral blood flow (CBF) and CBF self-regulation, impaired blood-brain barrier function, inflammatory factors, genes and environmental factors.

MXene/ZIF-67/PAN Nanofiber Film for Ultra-sensitive Pressure Sensors.

Flexible pressure sensors may be used in electronic skin (e-skin), artificial intelligence devices, and disease diagnosis, which require a large response range and high sensitivity. An appropriate design of the structure of the active layer can help effectively solve this problem. Herein, we aim at developing a wearable pressure sensor using the MXene/ZIF-67/polyacrylonitrile (PAN) nanofiber film, fabricated by electrospinning technology.

Cerebral blood flow self-regulation in depression.

Background: Depression is a common neuropsychiatric disease with a high prevalence rate. Sleep problems, memory decline, dizziness and headaches are the most common neurological symptoms in depressed patients. Abnormality of cerebral blood flow (CBF) has been observed in depressive patients, but those patients did not have intracranial structural damage.

Knitted TiCT MXene based fiber strain sensor for human-computer interaction.

Fiber-based stretchable electronics with feasibility of weaving into textiles and advantages of light-weight, long-term stability, conformability and easy integration are highly desirable for wearable electronics to realize personalized medicine, artificial intelligence and human health monitoring. Herein, a fiber strain sensor is developed based on the TiCT MXene wrapped by poly(vinylidenefluoride-co-trifluoroethylene) (P(VDF-TrFE)) polymer nanofibers prepared via electrostatic spinning. Owing to the good conductivity of TiCT and unique 3D reticular structure with wave shape, the resistance of TiCT@P(VDF-TrFE) polymer nanofibers changes under external force, thus providing remarkable strain inducted sensing performance.

Flexible Self-Powered Integrated Sensing System with 3D Periodic Ordered Black Phosphorus@MXene Thin-Films.

Accurate and continuous detection of physiological signals without the need for an external power supply is a key technology for realizing wearable electronics as next-generation biomedical devices. Herein, it is shown that a MXene/black phosphorus (BP)-based self-powered smart sensor system can be designed by integrating a flexible pressure sensor with direct-laser-writing micro-supercapacitors and solar cells. Using a layer-by-layer (LbL) self-assembly process to form a periodic interleaving MXene/BP lamellar structure results in a high energy-storage capacity in a direct-laser-writing micro-supercapacitor to drive the operation of sensors and compensate the intermittency of light illumination.